def main():
# read all tweets and labels
fp = open( 'face_tweets.csv', 'rb' )
reader = csv.reader( fp, delimiter=',', quotechar='"', escapechar='\\' )
tweets = []
for row in reader:
tweets.append( [row[1], row[0]] )
fp.close()
# # treat neutral and irrelevant the same
# for t in tweets:
# if t[1] == 'irrelevant':
# t[1] = 'neutral'
random.shuffle( tweets );
fvecs = [(tweet_features.make_tweet_dict(t),s) for (t,s) in tweets]
# dump tweets which our feature selector found nothing
#for i in range(0,len(tweets)):
# if tweet_features.is_zero_dict( fvecs[i][0] ):
# print tweets[i][1] + ': ' + tweets[i][0]
# apply PCA reduction
#(v_train, v_test) = \
# tweet_pca.tweet_pca_reduce( v_train, v_test, output_dim=1.0 )
basic_analyze(fvecs)
cross_validate(fvecs, 10, 0.8)
def main():
# read all tweets and labels
fp = open('face_tweets.csv', 'rb')
reader = csv.reader(fp, delimiter=',', quotechar='"', escapechar='\\')
tweets = []
for row in reader:
tweets.append([row[1], row[0]])
fp.close()
# # treat neutral and irrelevant the same
# for t in tweets:
# if t[1] == 'irrelevant':
# t[1] = 'neutral'
random.shuffle(tweets)
fvecs = [(tweet_features.make_tweet_dict(t), s) for (t, s) in tweets]
# dump tweets which our feature selector found nothing
#for i in range(0,len(tweets)):
# if tweet_features.is_zero_dict( fvecs[i][0] ):
# print tweets[i][1] + ': ' + tweets[i][0]
# apply PCA reduction
#(v_train, v_test) = \
# tweet_pca.tweet_pca_reduce( v_train, v_test, output_dim=1.0 )
basic_analyze(fvecs)
cross_validate(fvecs, 10, 0.8)
for row in reader:
tweets.append( [row[1], row[0]] );
print(row[1])
print(tweets[0:50])
# treat neutral and irrelevant the same
for t in tweets:
print(t[0])
if t[0] == 'irrelevant':
t[0] = 'neutral'
# split in to training and test sets
random.shuffle( tweets );
fvecs = [(tweet_features.make_tweet_dict(s),t) for (t,s) in tweets]
v_train = fvecs[:375]
v_test = fvecs[375:]
#print(str(v_train))
#print(str(v_test))
for i in range(0, 20):
print(fvecs[i])
# dump tweets which our feature selector found nothing
tot = 0;
for i in range(0,len(tweets)):
if tweet_features.is_zero_dict( fvecs[i][0] ):
#print(tweets[i][1] + ': ' + tweets[i][0])
tot = tot + 1
print(tot)
# read all tweets and labels
fp = open('sentiment.csv', 'rb')
reader = csv.reader(fp, delimiter=',', quotechar='"', escapechar='\\')
tweets = []
for row in reader:
tweets.append([row[3], row[4]])
# treat neutral and irrelevant the same
for t in tweets:
if t[1] == 'irrelevant':
t[1] = 'neutral'
# split in to training and test sets
random.shuffle(tweets)
fvecs = [(tweet_features.make_tweet_dict(t), s) for (t, s) in tweets]
v_train = fvecs[:2500]
v_test = fvecs[2500:]
# dump tweets which our feature selector found nothing
#for i in range(0,len(tweets)):
# if tweet_features.is_zero_dict( fvecs[i][0] ):
# print tweets[i][1] + ': ' + tweets[i][0]
# apply PCA reduction
#(v_train, v_test) = \
# tweet_pca.tweet_pca_reduce( v_train, v_test, output_dim=1.0 )
# train classifier
classifier = nltk.NaiveBayesClassifier.train(v_train)
#classifier = nltk.classify.maxent.train_maxent_classifier_with_gis(v_train);
text = nltk.word_tokenize(row[1], language='english')
text = nltk.pos_tag(text)
tweets.append([text,row[4]])
#added: pos tag is added as feature
print tweets
# treat neutral and irrelevant the same
for t in tweets:
if t[1] == 'irrelevant':
t[1] = 'neutral'
# split in to training and test sets
random.shuffle(tweets)
#pdb.set_trace()
fvecs = [(tweet_features.make_tweet_dict(t),s) for (t,s) in tweets]
v_train = fvecs[:10]
v_test = fvecs[:10]
# dump tweets which our feature selector found nothing
#for i in range(0,len(tweets)):
# if tweet_features.is_zero_dict( fvecs[i][0] ):
# print tweets[i][1] + ': ' + tweets[i][0]
# apply PCA reduction
(v_train, v_test) = \
tweet_pca.tweet_pca_reduce( v_train, v_test, output_dim=1.0 )